Are you looking to enhance your trading strategies with the power of Python and machine learning? PyBroker is a Python framework designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance.
- A super-fast backtesting engine built in NumPy and accelerated with Numba.
- Easy creation of trading rules and models for executing across multiple instruments.
- Integration of trading signals across multiple time intervals, including daily, weekly, and monthly.
- Access to historical data from Alpaca, Yahoo Finance, AKShare, or from your own data provider.
- Model training and backtesting using Walkforward Analysis, which simulates how the strategy would perform during actual trading.
- Reliable trading metrics that use randomized bootstrapping to provide more accurate results.
- Parameter optimization with Optuna to select the best strategy parameters.
- Caching of downloaded data, indicators, and models to speed up your development process.
- Parallelized computation and training for faster performance.
- Agent Skills that help AI agents write trading strategies and backtests using PyBroker.
PyBroker provides you with the tools to build, test, and evaluate algorithmic trading strategies backed by machine learning.
PyBroker supports Python 3.11+ on Windows, Mac, and Linux. You can install
PyBroker using pip:
pip install -U lib-pybrokerOr you can clone the Git repository with:
git clone https://github.com/edtechre/pybrokerHere's a glimpse of what backtesting with PyBroker looks like with these code snippets:
Rule-based Strategy:
from pybroker import Strategy, YFinance, highest
def exec_fn(ctx):
# Get the rolling 10 day high.
high_10d = ctx.indicator('high_10d')
# Buy on a new 10 day high.
if not ctx.long_pos() and high_10d[-1] > high_10d[-2]:
ctx.buy_shares = 100
# Hold the position for 5 days.
ctx.hold_bars = 5
# Set a stop loss of 2%.
ctx.stop_loss_pct = 2
strategy = Strategy(YFinance(), start_date='1/1/2025', end_date='8/1/2026')
strategy.add_execution(
exec_fn, ['AAPL', 'MSFT'], indicators=highest('high_10d', 'close', period=10))
# Run the backtest after 20 days have passed.
result = strategy.backtest(warmup=20)Model-based Strategy:
import pybroker
from pybroker import Alpaca, Strategy
def train_fn(symbol, train_data, test_data):
# Train the model using indicators stored in train_data.
...
return trained_model
# Register the model and its training function with PyBroker.
my_model = pybroker.model('my_model', train_fn, indicators=[...])
def exec_fn(ctx):
preds = ctx.preds('my_model')
if not ctx.long_pos() and preds[-1] > buy_threshold:
ctx.buy_shares = 100
elif ctx.long_pos() and preds[-1] < sell_threshold:
ctx.sell_all_shares()
alpaca = Alpaca(api_key=..., api_secret=...)
strategy = Strategy(alpaca, start_date='1/1/2025', end_date='8/1/2026')
strategy.add_execution(exec_fn, ['AAPL', 'MSFT'], models=my_model)
# Run Walkforward Analysis on 1 minute data using 5 windows with 50/50 train/test data.
result = strategy.walkforward(timeframe='1m', windows=5, train_size=0.5)- Getting Started with Data Sources
- Backtesting a Strategy
- Evaluating with Bootstrap Metrics
- Ranking Long and Short Signals
- Writing Indicators
- Training a Model
- Creating a Custom Data Source
- Applying Stops
- Rebalancing Positions
- Rotational Trading
- Configuring Parallelization
- Parameter Optimization
- Margin Trading
- Modeling Slippage
- Multiple Time Intervals
- Time Series Models
- Multi-Symbol Models
- Dynamic Symbol Selection
- Agent Skills
- FAQs
PyBroker v2 now includes AI agent skills for coding agents:
- Strategy Creator
- Indicator Creator
- Model Trainer
- Parameter Optimization
- Multi-Interval Strategies
- Rotational Trading
The full reference documentation is hosted at www.pybroker.com.
(For Chinese users: 中文文档, courtesy of Albert King.)
